Updated documents following review suggestions

Added symlinks in all the templates pointing to the local template with the .gitignore file

Signed-off-by: Theodor Mihalache <tmihalac@redhat.com>

docs/getting-started/concepts/project.md

@@ -6,7 +6,7 @@ Projects provide complete isolation of feature stores at the infrastructure leve
 
 Users define one or more [feature views](feature-view.md) within a project. Each feature view contains one or more [features](feature-view.md#field). These features typically relate to one or more [entities](entity.md). A feature view must always have a [data source](data-ingestion.md), which in turn is used during the generation of training [datasets](feature-retrieval.md#dataset) and when materializing feature values into the online store.
 
-The concept of project provide the following benefits:
+The concept of a "project" provide the following benefits:
 
 **Logical Grouping**: Projects group related features together, making it easier to manage and track them.
 

docs/getting-started/quickstart.md

@@ -4,6 +4,12 @@
 
 Feast (Feature Store) is an open-source feature store designed to facilitate the management and serving of machine learning features in a way that supports both batch and real-time applications.
 
+* *For Data Scientists*: Feast is a a tool where you can easily define, store, and retrieve your features for both model development and model deployment. By using Feast, you can focus on what you do best: build features that power your AI/ML models and maximize the value of your data.
+
+* *For MLOps Engineers*: Feast is a library that allows you to connect your existing infrastructure (e.g., online database, application server, microservice, analytical database, and orchestration tooling) that enables your Data Scientists to ship features for their models to production using a friendly SDK without having to be concerned with software engineering challenges that occur from serving real-time production systems. By using Feast, you can focus on maintaining a resilient system, instead of implementing features for Data Scientists.
+
+* *For Data Engineers*: Feast provides a centralized catalog for storing feature definitions allowing one to maintain a single source of truth for feature data. It provides the abstraction for reading and writing to many different types of offline and online data stores. Using either the provided python SDK or the feature server service, users can write data to the online and/or offline stores and then read that data out again in either low-latency online scenarios for model inference, or in batch scenarios for model training.
+
 For more info refer to [Introduction to feast](../README.md)
 
 ## Prerequisites
@@ -26,7 +32,9 @@ In this tutorial we will:
 5. Read the latest features from the online store for real-time inference.
 6. Explore the (experimental) Feast UI
 
-***Note*** - Feast can used as an executable or as a server, please refer to [feast feature server](../reference/feature-servers/python-feature-server.md)
+***Note*** - Feast provides a python SDK as well as an optional [hosted service](../reference/feature-servers/python-feature-server.md) for reading and writing feature data to the online and offline data stores. The latter might be useful when non-python languages are required.
+
+For this tutorial, we will be using the python SDK.
 
 In this tutorial, we'll use Feast to generate training data and power online model inference for a 
 ride-sharing driver satisfaction prediction model. Feast solves several common issues in this flow:

sdk/python/feast/templates/athena/.gitignore

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+../local/.gitignore

sdk/python/feast/templates/aws/.gitignore

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+../local/.gitignore

sdk/python/feast/templates/cassandra/.gitignore

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+../local/.gitignore

sdk/python/feast/templates/gcp/.gitignore

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+../local/.gitignore

sdk/python/feast/templates/hazelcast/.gitignore

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+../local/.gitignore

sdk/python/feast/templates/hbase/.gitignore

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+../local/.gitignore

sdk/python/feast/templates/local/.gitignore

@@ -17,6 +17,7 @@ venv.bak/
 *.egg-info/
 dist/
 build/
+.venv
 
 # Pytest
 .cache

sdk/python/feast/templates/minimal/.gitignore

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+../local/.gitignore